Tootfinder

The Problem with Safety Classification is not just the Models
Sowmya Vajjala
https://arxiv.org/abs/2507.21782 https://arxiv.org/pdf/2507.21782

The Problem with Safety Classification is not just the Models
Studying the robustness of Large Language Models (LLMs) to unsafe behaviors is an important topic of research today. Building safety classification models or guard models, which are fine-tuned models for input/output safety classification for LLMs, is seen as one of the solutions to address the issue. Although there is a lot of research on the safety testing of LLMs themselves, there is little research on evaluating the effectiveness of such safety classifiers or the evaluation datasets used fo…

Presenting a classifier to detect research contributions in OpenAlex
Nick Haupka
https://arxiv.org/abs/2507.22479 https://arxiv.org/pdf/2507.22479

Presenting a classifier to detect research contributions in OpenAlex
This paper introduces a document type classifier with the purpose to optimise the distinction between research and non-research journal publications in OpenAlex. Based on open metadata, the classifier can detect non-research or editorial content within a set of classified articles and reviews (e.g. paratexts, abstracts, editorials, letters). The classifier achieves an F1-score of 0,95, indicating a potential improvement in the data quality of bibliometric research in OpenAlex when applying the …

Multimodal Late Fusion Model for Problem-Solving Strategy Classification in a Machine Learning Game
Clemens Witt, Thiemo Leonhardt, Nadine Bergner, Mareen Grillenberger
https://arxiv.org/abs/2507.22426

Multimodal Late Fusion Model for Problem-Solving Strategy Classification in a Machine Learning Game
Machine learning models are widely used to support stealth assessment in digital learning environments. Existing approaches typically rely on abstracted gameplay log data, which may overlook subtle behavioral cues linked to learners' cognitive strategies. This paper proposes a multimodal late fusion model that integrates screencast-based visual data and structured in-game action sequences to classify students' problem-solving strategies. In a pilot study with secondary school students (N=149) p…

Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Ajay Mittal, Raghav Mehta, Omar Todd, Philipp Seeb\"ock, Georg Langs, Ben Glocker
https://arxiv.org/abs/2506.22226

Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challe…

Usually, clickbait titles repel me as a reader/viewer. I tend to skip.
Occasionally, though, I do click on them. Maybe what's behing the clickbait is actually interesting?
I does happen. Sometimes I don't regret clicking (but I hardly ever share).
Most often, though, I do.

ABC-SN: Attention Based Classifier for Supernova Spectra
Willow Fox Fortino, Federica B. Bianco, Pavlos Protopapas, Daniel Muthukrishna, Austin Brockmeier
https://arxiv.org/abs/2507.22106

ABC-SN: Attention Based Classifier for Supernova Spectra
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques (Blondin & Tonry 2007) and require significant human supervision. Two deep learning spe…

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Johan Hallberg Szabadv\'ary, Tuwe L\"ofstr\"om, Ulf Johansson, Cecilia S\"onstr\"od, Ernst Ahlberg, Lars Carlsson
https://arxiv.org/abs/2506.21802

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue by abstaining from making a prediction if it is likely to be incorrect. In this work, we formalise the approach to ML with reject option in binary classification, deriving theoretical guarantees on the resulting error rate. This is achieved through conformal pred…

On Lie's classification of nonsolvable subalgebras of vector fields on the plane
Hassan Azad, Indranil Biswas, Ahsan Fazil, Fazal M. Mahomed
https://arxiv.org/abs/2507.22642

On Lie's classification of nonsolvable subalgebras of vector fields on the plane
A brief proof of Lie's classification of finite dimensional subalgebras of vector fields on the complex plane that have a proper Levi decomposition is given. The proof uses basic representation theory of sl(2, C). This, combined with \cite{ABF2} and \cite{ABF3} completes the classification of finite dimensional subalgebras of vector fields on the complex plane.

Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
Kunal Kawadkar
https://arxiv.org/abs/2507.21156 https://

Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
The emergence of Vision Transformers (ViTs) has revolutionized computer vision, yet their effectiveness compared to traditional Convolutional Neural Networks (CNNs) in medical imaging remains under-explored. This study presents a comprehensive comparative analysis of CNN and ViT architectures across three critical medical imaging tasks: chest X-ray pneumonia detection, brain tumor classification, and skin cancer melanoma detection. We evaluated four state-of-the-art models - ResNet-50, Efficien…

CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification
Black Sun (Delia), Die (Delia), Hu
https://arxiv.org/abs/2507.22205 htt…

CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification
Remote fetal monitoring technologies are becoming increasingly common. Yet, most current systems offer limited interpretability, leaving expectant parents with raw cardiotocography (CTG) data that is difficult to understand. In this work, we present CTG-Insight, a multi-agent LLM system that provides structured interpretations of fetal heart rate (FHR) and uterine contraction (UC) signals. Drawing from established medical guidelines, CTG-Insight decomposes each CTG trace into five medically def…